
    sg                     l    d Z ddlmZ ddlmZ ddlmZmZ  ej                  e	      Z
 G d dee      Zy)zConvNeXTV2 model configuration   )PretrainedConfig)logging)BackboneConfigMixin*get_aligned_output_features_output_indicesc                   @     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )ConvNextV2Configa  
    This is the configuration class to store the configuration of a [`ConvNextV2Model`]. It is used to instantiate an
    ConvNeXTV2 model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the ConvNeXTV2
    [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        patch_size (`int`, *optional*, defaults to 4):
            Patch size to use in the patch embedding layer.
        num_stages (`int`, *optional*, defaults to 4):
            The number of stages in the model.
        hidden_sizes (`List[int]`, *optional*, defaults to `[96, 192, 384, 768]`):
            Dimensionality (hidden size) at each stage.
        depths (`List[int]`, *optional*, defaults to `[3, 3, 9, 3]`):
            Depth (number of blocks) for each stage.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
            `"selu"` and `"gelu_new"` are supported.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        drop_path_rate (`float`, *optional*, defaults to 0.0):
            The drop rate for stochastic depth.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        out_features (`List[str]`, *optional*):
            If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
            (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
            corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
            same order as defined in the `stage_names` attribute.
        out_indices (`List[int]`, *optional*):
            If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
            many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
            If unset and `out_features` is unset, will default to the last stage. Must be in the
            same order as defined in the `stage_names` attribute.

    Example:
    ```python
    >>> from transformers import ConvNeXTV2Config, ConvNextV2Model

    >>> # Initializing a ConvNeXTV2 convnextv2-tiny-1k-224 style configuration
    >>> configuration = ConvNeXTV2Config()

    >>> # Initializing a model (with random weights) from the convnextv2-tiny-1k-224 style configuration
    >>> model = ConvNextV2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
convnextv2c                    t        |   di | || _        || _        || _        |g dn|| _        |g dn|| _        || _        || _        || _	        |	| _
        |
| _        dgt        dt        | j                        dz         D cg c]  }d| 	 c}z   | _        t        ||| j                        \  | _        | _        y c c}w )N)`      i  i   )r   r   	   r   stem   stage)out_featuresout_indicesstage_names )super__init__num_channels
patch_size
num_stageshidden_sizesdepths
hidden_actinitializer_rangelayer_norm_epsdrop_path_rate
image_sizerangelenr   r   _out_features_out_indices)selfr   r   r   r   r   r   r   r   r   r    r   r   kwargsidx	__class__s                  j/var/www/html/venv/lib/python3.12/site-packages/transformers/models/convnextv2/configuration_convnextv2.pyr   zConvNextV2Config.__init__T   s      	"6"($$3?3G/\&,nl&$!2,,$"8aT[[IY\]I]@^&_se}&__0Z%;DL\L\1
-D- '`s   	C)r      r*   NNgelug{Gz?g-q=g           NN)__name__
__module____qualname____doc__
model_typer   __classcell__)r(   s   @r)   r   r      s@    6p J 
 
    r   N)r0   configuration_utilsr   utilsr   utils.backbone_utilsr   r   
get_loggerr-   loggerr   r   r3   r)   <module>r9      s9    % 3  c 
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